Heading
DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The amendment filed June 5, 2026 has been entered. Claims 1, 3, 8, 10, 22, and 24 are presently amended. The remaining claims are in original or previously presented form. Therefore, claims 1-14 and 22-28 are pending in the application. Claims 1, 8, and 22 are the independent claims.
The Remarks filed June 5, 2026 have been fully considered. The applicant argues under the heading “I. CLAIM OBJECTIONS” that claims 3, 10, 22, and 24 have been amended to address the issue raised in the last detailed action, which was the Non-Final Rejection dated March 5, 2026. The examiner agrees and withdraws the objections.
The applicant argues under the heading “II. CLAIM REJECTIONS – 35 USC § 102” that the presently amended independent claims are not anticipated by Yan et al. (US2025/0200979). The applicant summarizes claim 1 on page 10 of the Remarks and relates the predicted trajectory to Fig. 3D. The applicant notes that the system will cause the vehicle to move along in accordance with “one or more target predictions,” according to the Remarks.
According to the present specification, paragraph 0142 points P1, P2, and P3 in Fig. 3D “may not correspond to—or may not coincide with—predicted positions of the ego vehicle. Rather, these points are control points used to construct the Bezier curve as the predicted trajectory.”
The examiner agrees that Yan does not explicitly teach the presently amended clause of present claim 1. Yet the examiner has identified prior art that teaches these limitations. Due to amendment the grounds for rejection have changed. Please see the rejections below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-14 and 22-28 are rejected under 35 U.S.C. 103 as being unpatentable over Yan et al. (US2025/0200979) in view of Oh (US2022/0204041).
Regarding claim 1, Yan teaches:
A method comprising:
determining, from a plurality of physical object observations derived from sensor-acquired data and non-sensor-acquired data collected for a vehicle, a plurality of input feature vectors in a latent space of three dimensions (see Yan paragraph 0014 for the sensors on the host vehicle. See Fig. 2 for sensing data 210, traffic lights 220, and map information 124. See paragraph 0032 for additional sensors. See paragraph 0052 for “feature vectors” in “different dimensions”. See paragraph 0054 for the “feature vector” including in “a multi-dimensional embedding space”. See paragraph 0058 for tokens in three dimensions.),
wherein the three dimensions include
a time dimension (see paragraph 0053 for a “temporal dimension”),
a feature dimension and (the present published disclosure, Shamsoshoara (US2025/0196892) uses the word “feature” extremely broadly. Paragraph 0125 refers to “different feature types (e.g., different vehicles/objects)”. With that in mind, see Yan, paragraph 0052 for “feature vectors” such as roadgraph information having a dimension Drx1.)
an embedding size dimension (in the present published disclosure see paragraph 0101 for the “The embeddings or vectors may be of an equal length (or size/dimension) referred to as ‘embedding size’”. With that in mind, see Yan paragraph 0052 for “embeddings of a common dimension”.);
applying a plurality of attention heads implemented in one or more transformer networks of an artificial intelligence (AI)-based system to the input feature vectors in the latent space to generate attention scores forming a plurality of attention layers (in the present published disclosure, see Fig. 4, step. 404. What is an “attention head” in the disclosure? Paragraph 0067 recites that “multi-head attention (networks/blocks) may include…a plurality of attention heads each of which represents a query (Q), key (K) and value (V) (attention subnetwork as illustrated in Fig. 2C.” Paragraph 0068 recites that “Through these attention heads of QKV (attention) subnetworks (or sub-blocks), the multi-head attention (networks/blocks) in the attention-based model 204 or the transformer (neural network) in the AI tactical planner 102 can process input vectors or embeddings represented in a late space 208 of Fig. 2C.” Paragraph 0070 refers to “the attention heads of QKV subnetworks can operate in parallel in real-time to pay attention across different features represented in the latent space 208”. Paragraph 0065 teaches the “attention-based model 204” and paragraph 0066 teaches that the “multi-head (self) attention networks (blocks)…may be implemented as a…part of the attention-based model 204”, which can be seen in Fig. 2B. Paragraphs 0120-0121 teach “attention scores”. Paragraph 0127 teaches that “the attentions paid or attention scores computed” in model 204 can be switched or swapped between the two dimensions of feature and time in the latent space 208. This way “the AI-based models as described herein are trained or applied to respond to a wide variety of contextual, temporal or latent relations among or between values or features alongside the time axis/dimension as well as the features axis-dimension in the late space 208.”
With that in mind, see Yan paragraph 0056 for a neural network with an “attention-based transformed architecture” including spatial and temporal encoder blocks. See paragraph 0057 for multi-axes attention blocks that “compute attention scores”. Each attention block can including “one or more multi-head self-attention layers”. These blocks can be stacked together. See also Fig. 4);
generating, based at least in part on the attention scores in the plurality of attention layers, one or more target predictions relating to navigation operations of the vehicle (see paragraph 0033 for the system being configured for “selecting a particular path through the immediate driving environment, which can include selecting a traffic lane, negotiating a traffic congestion…and so on.” See Fig. 4 and paragraph 0071 for “predicted trajectories 442”.);
wherein the one or more target predictions include a prediction of one or more control points to construct a predicted trajectory of the vehicle between a starting point of the predicted trajectory and an ending point of the predicted trajectory (see Yan paragraph 0078 for generating trajectories for a vehicle’s “entire sequence” from t1 to tM.),
interacting with a driver assistant sub-system of the vehicle to cause a vehicle propulsion operation to be performed in accordance with the one or more target predictions (in the present disclosure see paragraph 0077 for the host vehicle having a lane change assistant (LCA) among other ADAS functions. See paragraph 0056 for the system potentially using “lane data and or object data represent[ing] history information of all vehicles and/or objects in the same scene or on the same road section traveled by the vehicle/ego up to the latest time or the current wall clock time.” See paragraph 0041 for the AT-based “target predictions” including lane change commands. See paragraphs 0043-0044 for predicting future lane changes and “passing the future lane change predictions…to the travel assistant to…perform specific lane changes as predicted.”
With that in mind, see Yan paragraph 0033 for the system being configured for “selecting a particular path through the immediate driving environment, which can include selecting a traffic lane, negotiating a traffic congestion…and so on.” See also paragraph 0017.).
Yet Yan does not explicitly further teach:
wherein the one or more control points do not coincide with any point in the predicted trajectory.
However, Oh teaches:
wherein the one or more control points do not coincide with any point in the predicted trajectory (see paragraph 0115 for generating a path for a lane change (an example of a trajectory in the present disclosure) using a Bezier curve generated by a neural network.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as taught by Yan, to add the additional features as indicated as being taught by Oh. The motivation for doing so would be to generate trajectories for irregular lane configurations that are safe in order to free vehicle occupants from the task, as recognized by Oh (see paragraphs 0005 and 0003).
This conclusion of obviousness corresponds to KSR rationale “A”: it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined prior art elements according to known methods to yield predictable results. See MPEP § 2141, subsection III.
The applicant on page 10 of the Remarks relates the predicted trajectory claimed to Fig. 3D. According to paragraph 0142 of the present specification, points P1, P2, and P3 in Fig. 3D “may not correspond to—or may not coincide with—predicted positions of the ego vehicle. Rather, these points are control points used to construct the Bezier curve as the predicted trajectory.” Oh (US2022/0204041) teaches generating a Bezier curve for a lane change. The examiner notes that Bezier curves inherently have points “wherein the one or more control points do not coincide with any point in the predicted trajectory,” as claimed.
Regarding claim 2, Yan and Oh teach the method of 1.
Yan further teaches:
The method of Claim 1, wherein
the plurality of attention heads includes
a first attention head configured to apply first self-attention to the plurality of input feature vectors across the time dimension of the latent space (see Yan paragraph 0056 for a neural network with an “attention-based transformed architecture” including spatial and temporal encoder blocks. This means there can be a spatial block and a temporal block. See paragraph 0057 for multi-axes attention blocks that “compute attention scores”. Each attention block can including “one or more multi-head self-attention layers”. These blocks can be stacked together. See also Fig. 4. See also paragraph 0053 for a “temporal dimension”.) and
a second attention head configured to concurrently apply second self-attention to the plurality of input feature vectors across the feature dimension of the latent space (see Yan paragraph 0056 for a neural network with an “attention-based transformed architecture” including spatial and temporal encoder blocks. This means there can be a spatial block and a temporal block. See paragraph 0057 for multi-axes attention blocks that “compute attention scores”. Each attention block can including “one or more multi-head self-attention layers”. These blocks can be stacked together. See also Fig. 4. See paragraph 0085 for the operations being performed “concurrently”).
Regarding claim 3, Yan and Oh teach the method of 1.
Yan further teaches:
The method of Claim 1, wherein
the [[AI]]AI-based system operates with a computer implemented traffic assistant to cause one or more vehicle propulsion operations to be controlled based at least in part on one or more of
vehicle velocity predictions (see paragraph 0031 for “predicted locations/velocities”. See paragraph 0088 for probable trajectories of the vehicle including “velocities of the vehicle”.) or
lane changes for the vehicle in the one or more target predictions (see paragraph 0031 for “predicted locations/velocities”. See paragraph 0088 for probable trajectories of the vehicle including “velocities of the vehicle”. See paragraph 0088 for “the acceleration/braking status, and “steering status, status of signaling” etc of the vehicle. See paragraph 0093 for determining that one predicted trajectory has a low probability of being completed, and therefore the system can “change driving lane” or perform some other “selected driving path”.).
Regarding claim 4, Yan and Oh teach the method of 1.
Yan further teaches:
The method of Claim 1, wherein
the sensor-acquired data is generated with a sensor stack deployed with the vehicle (see paragraph 0014);
wherein the sensor stack includes one or more of: in-vehicle cameras, image sensors, non-image sensors, radars, LIDARs, ultrasonic sensors, or infrared sensors (see paragraph 0014).
Regarding claim 5, Yan and Oh teach the method of 1.
Yan further teaches:
The method of Claim 1, wherein
the non-sensor-acquired data includes one or more lane images (see paragraph 0018 for input data including “roadgraph data (e.g., map data, lane boundaries, road signs, etc.)”. See also paragraph 0044, especially the first sentence. See also paragraph 0045 for a data repository 250 with roadgraphs and “images”.).
Regarding claim 6, Yan and Oh teach the method of 1.
Yan further teaches:
The method of Claim 1, wherein
the one or more target predictions are generated by one or more multi-layer perceptron neural networks based at least in part on the attention scores forming the plurality of attention layers as generated by the plurality of attention heads of the one or more transformer neural networks (see Yan paragraph 0056 for a neural network with an “attention-based transformed architecture” including spatial and temporal encoder blocks. See paragraph 0057 for multi-axes attention blocks that “compute attention scores”. Each attention block can including “one or more multi-head self-attention layers”. These blocks can be stacked together. See also Fig. 4. See paragraph 0057 for a “multilayer perception layers” that are “elements” of the neural network”).
Regarding claim 7, Yan and Oh teach the method of 1.
Yan further teaches:
The method of Claim 1, wherein
one or both of a car network graph in reference to the vehicle (see paragraph 0018) or a trajectory of the vehicle is generated based at least in part on the one or more target predictions (see paragraph 0093 for determining that one predicted trajectory has a low probability of being completed, and therefore the system can “change driving lane” or perform some other “selected driving path”. See paragraph 0017 for the model being able to determine who other road users will respond to a host vehicle’s driving path.).
Regarding claim 8, Yan teaches:
A system, comprising (see Figs. 1 and 2):
one or more computing processors (see Fig. 7, item 702);
one or more non-transitory computer readable media storing a program of instructions that is executable by the one or more computing processors to perform (see Fig. 7, item 704 and 718):
determining, from a plurality of physical object observations derived from sensor-acquired data and non-sensor-acquired data collected for a vehicle, a plurality of input feature vectors in a latent space of three dimensions (see Yan paragraph 0014 for the sensors on the host vehicle. See Fig. 2 for sensing data 210, traffic lights 220, and map information 124. See paragraph 0032 for additional sensors. See paragraph 0052 for “feature vectors” in “different dimensions”. See paragraph 0054 for the “feature vector” including in “a multi-dimensional embedding space”. See paragraph 0058 for tokens in three dimensions.),
wherein the three dimensions include
a time dimension (see paragraph 0053 for a “temporal dimension”),
a feature dimension and (the present published disclosure, Shamsoshoara (US2025/0196892) uses the word “feature” extremely broadly. Paragraph 0125 refers to “different feature types (e.g., different vehicles/objects)”. With that in mind, see Yan, paragraph 0052 for “feature vectors” such as roadgraph information having a dimension Drx1.)
an embedding size dimension (in the present published disclosure see paragraph 0101 for the “The embeddings or vectors may be of an equal length (or size/dimension) referred to as ‘embedding size’”. With that in mind, see Yan paragraph 0052 for “embeddings of a common dimension”.);
applying a plurality of attention heads implemented in one or more transformer networks of an artificial intelligence (AI)-based to the input feature vectors in the latent space to generate attention scores forming a plurality of attention layers (see Yan paragraph 0056 for a neural network with an “attention-based transformed architecture” including spatial and temporal encoder blocks. See paragraph 0057 for multi-axes attention blocks that “compute attention scores”. Each attention block can including “one or more multi-head self-attention layers”. These blocks can be stacked together. See also Fig. 4);
generating, based at least in part on the attention scores in the plurality of attention layers, one or more target predictions relating to navigation operations of the vehicle (see paragraph 0033 for the system being configured for “selecting a particular path through the immediate driving environment, which can include selecting a traffic lane, negotiating a traffic congestion…and so on.” See Fig. 4 and paragraph 0071 for “predicted trajectories 442”.);
wherein the one or more target predictions include a prediction of one or more control points to construct a predicted trajectory of the vehicle between a starting point of the predicted trajectory and an ending point of the predicted trajectory (see Yan paragraph 0078 for generating trajectories for a vehicle’s “entire sequence” from t1 to tM.),
interacting with a driver assistant sub-system of the vehicle to cause a vehicle propulsion operation to be performed in accordance with the one or more target predictions (see Yan paragraph 0033 for the system being configured for “selecting a particular path through the immediate driving environment, which can include selecting a traffic lane, negotiating a traffic congestion…and so on.” See also paragraph 0017.).
Yet Yan does not explicitly further teach:
wherein the one or more control points do not coincide with any point in the predicted trajectory.
However, Oh teaches:
wherein the one or more control points do not coincide with any point in the predicted trajectory (see paragraph 0115 for generating a path for a lane change (an example of a trajectory in the present disclosure) using a Bezier curve generated by a neural network.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as taught by Yan, to add the additional features as indicated as being taught by Oh. The motivation for doing so would be to generate trajectories for irregular lane configurations that are safe in order to free vehicle occupants from the task, as recognized by Oh (see paragraphs 0005 and 0003).
This conclusion of obviousness corresponds to KSR rationale “A”: it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined prior art elements according to known methods to yield predictable results. See MPEP § 2141, subsection III.
The applicant on page 10 of the Remarks relates the predicted trajectory claimed to Fig. 3D. According to paragraph 0142 of the present specification, points P1, P2, and P3 in Fig. 3D “may not correspond to—or may not coincide with—predicted positions of the ego vehicle. Rather, these points are control points used to construct the Bezier curve as the predicted trajectory.” Oh (US2022/0204041) teaches generating a Bezier curve for a lane change. The examiner notes that Bezier curves inherently have points “wherein the one or more control points do not coincide with any point in the predicted trajectory,” as claimed.
Regarding claims 9-14, they are substantially similar to claims 2-7, respectively. Please see the rejections of those claims.
Regarding claim 22, Yan teaches:
One or more non-transitory computer readable media storing a program of instructions that is executable by one or more computing processors to perform (see Fig. 7, items 704 and 718):
determining, from a plurality of physical object observations derived from sensor- acquired data and non-sensor-acquired data collected for a vehicle, a plurality of input feature vectors in a latent space of three dimensions (see Yan paragraph 0014 for the sensors on the host vehicle. See Fig. 2 for sensing data 210, traffic lights 220, and map information 124. See paragraph 0032 for additional sensors. See paragraph 0052 for “feature vectors” in “different dimensions”. See paragraph 0054 for the “feature vector” including in “a multi-dimensional embedding space”. See paragraph 0058 for tokens in three dimensions.),
wherein the three dimensions include
a time dimension (see paragraph 0053 for a “temporal dimension”),
a feature dimension and (the present published disclosure, Shamsoshoara (US2025/0196892) uses the word “feature” extremely broadly. Paragraph 0125 refers to “different feature types (e.g., different vehicles/objects)”. With that in mind, see Yan, paragraph 0052 for “feature vectors” such as roadgraph information having a dimension Drx1.)
an embedding size dimension (in the present published disclosure see paragraph 0101 for the “The embeddings or vectors may be of an equal length (or size/dimension) referred to as ‘embedding size’”. With that in mind, see Yan paragraph 0052 for “embeddings of a common dimension”.);
applying a plurality of attention heads implemented in one or more transformer networks of an artificial intelligence (AI) based system to the input feature vectors in the latent space to generate attention scores forming a plurality of attention layers (see Yan paragraph 0056 for a neural network with an “attention-based transformed architecture” including spatial and temporal encoder blocks. See paragraph 0057 for multi-axes attention blocks that “compute attention scores”. Each attention block can including “one or more multi-head self-attention layers”. These blocks can be stacked together. See also Fig. 4);
generating, based at least in part on the attention scores in the plurality of attention layers, one or more target predictions relating to navigation operations of the vehicle (see paragraph 0033 for the system being configured for “selecting a particular path through the immediate driving environment, which can include selecting a traffic lane, negotiating a traffic congestion…and so on.” See Fig. 4 and paragraph 0071 for “predicted trajectories 442”.);
wherein the one or more target predictions include a prediction of one or more control points to construct a predicted trajectory of the vehicle between a starting point of the predicted trajectory and an ending point of the predicted trajectory (see Yan paragraph 0078 for generating trajectories for a vehicle’s “entire sequence” from t1 to tM.),
interacting with a driver assistant sub-system of the vehicle to cause a vehicle propulsion operation to be performed in accordance with the one or more target predictions (see Yan paragraph 0033 for the system being configured for “selecting a particular path through the immediate driving environment, which can include selecting a traffic lane, negotiating a traffic congestion…and so on.” See also paragraph 0017.).
Yet Yan does not explicitly further teach:
wherein the one or more control points do not coincide with any point in the predicted trajectory.
However, Oh teaches:
wherein the one or more control points do not coincide with any point in the predicted trajectory (see paragraph 0115 for generating a path for a lane change (an example of a trajectory in the present disclosure) using a Bezier curve generated by a neural network.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as taught by Yan, to add the additional features as indicated as being taught by Oh. The motivation for doing so would be to generate trajectories for irregular lane configurations that are safe in order to free vehicle occupants from the task, as recognized by Oh (see paragraphs 0005 and 0003).
This conclusion of obviousness corresponds to KSR rationale “A”: it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined prior art elements according to known methods to yield predictable results. See MPEP § 2141, subsection III.
The applicant on page 10 of the Remarks relates the predicted trajectory claimed to Fig. 3D. According to paragraph 0142 of the present specification, points P1, P2, and P3 in Fig. 3D “may not correspond to—or may not coincide with—predicted positions of the ego vehicle. Rather, these points are control points used to construct the Bezier curve as the predicted trajectory.” Oh (US2022/0204041) teaches generating a Bezier curve for a lane change. The examiner notes that Bezier curves inherently have points “wherein the one or more control points do not coincide with any point in the predicted trajectory,” as claimed.
Regarding claims 23-28, they are substantially similar to claims 2-7, respectively. Please see the rejections of those claims.
Additional Art
The prior art made of record here, though not relied upon, is considered pertinent to the present disclosure.
Eichberger (DE 10 2016 204957A1). Teaches at least generating Bezier-curve-based trajectories for vehicle lane changes.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL M. ROBERT whose telephone number is (571)270-5841. The examiner can normally be reached M-F 7:30-4:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached at 571-272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL M. ROBERT/Primary Examiner, Art Unit 3665
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